r2

summary of best predictions
mdlpreds mean(r2) median(r2) sd(r2)
phv 0.52 0.56 0.09
phvfsca 0.76 0.77 0.06
phvaso 0.67 0.71 0.17
phvasofsca 0.79 0.79 0.09

phv

phvfsca

phvaso

phvasofsca

all model boxplots

% MAE

summary of best predictions
mdlpreds mean(pctmae) median(pctmae) sd(pctmae)
phv 59.38 54.39 21.96
phvfsca 37.14 37.61 11.93
phvaso 53.98 45.90 23.32
phvasofsca 36.21 35.70 12.32

phv

phvfsca

phvaso

phvasofsca

all model boxplots

are the differences in patterns of fsca and swe similar? can fsca patterns be used to pick the date to predict swe?

comparison of maps for each date but leaving out maps from the same year in the comparison

with mean absolute error

with mean squared error

more dates are statistically significant with mae so we’ll stick with that.

compare prediction errors vs basin aggregated snow variables

compare with best errors

this doesn’t show much. errors go up end of season while mean swe goes down….

compare with all errors

compare prediction errors vs fsca pattern differences

compare with best errors only

again not a very useful plot. errors go up as fsca mae goes down (due to smaller differences when there is less snow cover)

compare with all errors by date

see “figs/so_many_fscaplots.pdf” for all plots

Table of significant slopes

simdte phv phvfsca phvaso phvasofsca sep simdte.1 phv.1 phvfsca.1 phvaso.1 phvasofsca.1
2013-04-03 -0.24 -0.07 -0.26 - | 2013-04-03 87.25 - 113.44 -
2013-04-29 -0.24 - -0.32 -0.17 | 2013-04-29 73.76 - 100.15 -
2013-05-03 -0.19 - - - | 2013-05-03 60.13 - 67.53 -
2013-05-25 - -0.09 -0.18 -0.18 | 2013-05-25 163.89 - 213.94 -
2013-06-01 -0.09 -0.15 -0.16 -0.24 | 2013-06-01 285.64 - 360.21 -
2013-06-08 -0.13 -0.25 -0.19 -0.37 | 2013-06-08 618.97 157.38 762.56 -
2014-03-23 -0.20 - -0.16 - | 2014-03-23 74.09 -52.27 97.27 -51.56
2014-04-07 -0.20 - -0.17 - | 2014-04-07 72.32 -54.53 92.60 -52.38
2014-04-13 -0.15 - -0.12 - | 2014-04-13 56.55 -65.95 87.28 -54.56
2014-04-20 - - - - | 2014-04-20 - - 93.80 -
2014-04-28 - - - -0.20 | 2014-04-28 - - 81.06 -
2014-05-02 - - - - | 2014-05-02 - - - -
2014-05-11 - - - - | 2014-05-11 - - - -
2014-05-17 -0.28 -0.20 -0.41 -0.44 | 2014-05-17 - - - -
2014-05-27 -0.29 -0.28 -0.42 -0.49 | 2014-05-27 499.44 - 670.84 -
2014-05-31 -0.27 -0.30 -0.37 -0.54 | 2014-05-31 730.34 - 935.01 -
2015-02-17 -0.54 -0.33 -0.50 - | 2015-02-17 - - - -
2015-03-05 -0.29 -0.11 -0.32 -0.17 | 2015-03-05 - - - -
2015-03-25 - - - 0.08 | 2015-03-25 - - 292.38 -
2015-04-09 -0.16 -0.19 -0.22 -0.24 | 2015-04-09 - - - -
2015-04-15 - - - - | 2015-04-15 254.24 - 358.00 -
2015-04-27 -0.28 -0.10 -0.21 - | 2015-04-27 - - - -
2015-05-01 - - -0.07 -0.12 | 2015-05-01 546.20 - 735.10 -
2015-06-08 -0.14 -0.23 -0.19 -0.57 | 2015-06-08 1298.93 377.43 1466.82 543.49
2016-05-27 -0.12 -0.12 - -0.15 | 2016-05-27 73.24 - 86.43 -
2016-06-07 -0.19 - - - | 2016-06-07 54.77 - 67.80 -
2016-06-13 -0.16 - -0.18 -0.16 | 2016-06-13 109.99 - 158.43 -
2016-06-20 -0.11 -0.15 -0.17 -0.30 | 2016-06-20 183.25 28.92 261.79 19.85

combine pred error best fit lines by mdlpreds, plot mean +/- se

compare best pred vs lowestfscamae pred

summary stats for differences between r2 of bestpreds and lowestfscamae pred
mdlpreds var mean(val) sd(val)
phv diff -0.03 0.02
phvaso diff -0.04 0.04
phvasofsca diff -0.04 0.06
phvfsca diff -0.02 0.02
summary stats of r2 for different modeltypes
errorvar mdlpreds var mean(val, na.rm = T) sd(val, na.rm = T)
r2 phv bestpred 0.52 0.09
r2 phv lowestfscamae 0.50 0.10
r2 phvaso bestpred 0.67 0.17
r2 phvaso lowestfscamae 0.63 0.18
r2 phvasofsca bestpred 0.79 0.09
r2 phvasofsca lowestfscamae 0.75 0.10
r2 phvfsca bestpred 0.76 0.06
r2 phvfsca lowestfscamae 0.74 0.06
summary stats for differences between pctmae of bestpreds and lowestfscamae pred
mdlpreds var mean(val) sd(val)
phv diff 22.70 38.00
phvaso diff 23.83 34.42
phvasofsca diff 30.42 29.96
phvfsca diff 29.93 40.43
summary stats for pctmae different modeltypes
errorvar mdlpreds var mean(val, na.rm = T) sd(val, na.rm = T)
pctmae phv bestpred 59.38 21.96
pctmae phv lowestfscamae 82.08 39.06
pctmae phvaso bestpred 53.98 23.32
pctmae phvaso lowestfscamae 77.81 37.47
pctmae phvasofsca bestpred 36.21 12.32
pctmae phvasofsca lowestfscamae 66.63 27.49
pctmae phvfsca bestpred 37.14 11.93
pctmae phvfsca lowestfscamae 67.06 39.15

swe and fsca difference for best predictions

More yellow means prediction was better. x and y axis represent the differences between simulation date and model date expressed as mean squared error or mean absolute error.

Again not that useful to look at because prediction error is lowest when fscamae is highest (due to more snow cover earlier in season). Not immediately intuitive until you look at fscamae vs date.